Table 2, 3, and 4
age<-Perception_propt%>%
mutate(Duration_infertility=recode(X6..Duration.of.infertility,"Nil:"="Nil"),yes_no=ifelse(
Duration_infertility=="Nil","Fertile","Infertile"))%>%group_by(yes_no)%>%
count(age=SECTION.A..SOCIO.DEMOGRAPHIC)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
column_to_rownames(var="age")%>%mutate(p_value=c("=0.04","","",""))
Gender<-Perception_propt%>%
mutate(Duration_infertility=recode(X6..Duration.of.infertility,"Nil:"="Nil"),yes_no=ifelse(
Duration_infertility=="Nil","Fertile","Infertile"))%>%group_by(yes_no)%>%
count(age=X2..Gender)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
column_to_rownames(var="age")%>%mutate(p_value=c("0.58",""))
Religion<-Perception_propt%>%
mutate(Duration_infertility=recode(X6..Duration.of.infertility,"Nil:"="Nil"),yes_no=ifelse(
Duration_infertility=="Nil","Fertile","Infertile"))%>%group_by(yes_no)%>%
count(age=X3..Religion)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
column_to_rownames(var="age")%>%mutate(p_value=c("0.51",""))
Occupation<-Perception_propt%>%
mutate(Duration_infertility=recode(X6..Duration.of.infertility,"Nil:"="Nil"),yes_no=ifelse(
Duration_infertility=="Nil","Fertile","Infertile"))%>%group_by(yes_no)%>%
count(age=X4..Occupation)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
column_to_rownames(var="age")%>%mutate(Fertile=str_replace_na(Fertile,"0"))%>%
mutate(p_value=c("=0.03","","","","",""))
Occupation$Fertile<-as.integer(Occupation$Fertile)
Occupation$Infertile<-as.integer(Occupation$Infertile)
Level_Education<-Perception_propt%>%
mutate(Duration_infertility=recode(X6..Duration.of.infertility,"Nil:"="Nil"),yes_no=ifelse(
Duration_infertility=="Nil","Fertile","Infertile"))%>%group_by(yes_no)%>%
count(age=X5..Level.of.education)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
column_to_rownames(var="age")%>%mutate(p_value=c("0.48","","",""))
Duration_of_Infertility<-Perception_propt%>%
mutate(Duration_infertility=recode(X6..Duration.of.infertility,"Nil:"="Nil"),
yes_no=ifelse(Duration_infertility=="Nil","Fertile","Infertile"))%>%
group_by(yes_no)%>%count(age=Duration_infertility)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
column_to_rownames(var="age")%>%
mutate(Fertile=str_replace_na(Fertile,"0"),
Infertile=str_replace_na(Infertile,"0"))%>%
mutate(p_value=c("<0.001","","","",""))
Duration_of_Infertility$Fertile<-as.integer(Duration_of_Infertility$Fertile)
Duration_of_Infertility$Infertile<-as.integer(Duration_of_Infertility$Infertile)
Fertile<-as.integer(Duration_of_Infertility$Fertile)
bind_rows(Age=age,
Gender=Gender,
Religion=Religion,Occupation=Occupation,Level_Education=Level_Education,
Duration_of_Infertility=Duration_of_Infertility,.id = "Variable")
## Variable Fertile Infertile p_value
## 26-35 Age 30 60 =0.04
## 36-45 Age 26 74
## <25 years Age 13 24
## >45 Age 4 27
## Female Gender 46 124 0.58
## Male Gender 27 61
## Christian Religion 42 95 0.51
## Muslim Religion 31 90
## Civil servant Occupation 2 4 =0.03
## Civil servant: Public sector Occupation 18 64
## Private sector Occupation 31 50
## Self employed Occupation 13 42
## Student Occupation 6 9
## Unemployed Occupation 3 16
## Informal Level_Education 1 3 0.48
## Primary Level_Education 2 7
## Secondary Level_Education 6 30
## Tertiary Level_Education 64 145
## Nil Duration_of_Infertility 73 0 <0.001
## 1-5 years Duration_of_Infertility 0 104
## 11-15 years Duration_of_Infertility 0 17
## 16-20 years Duration_of_Infertility 0 10
## 6-10 years Duration_of_Infertility 0 54
# Table 2 Knowledge and common misconceptions about factors that
# may affect sterility
# Common missconception about infertility
Common_MisConcept_About_Infertility<-Perception_propt%>%
separate(X13..Common.misconception.about.the.causes.of.infertility...Tick.as.many.as.apply.,c("an1","an2","an3","an4"),sep = ";")%>%
select(an1,an2,an3,an4)%>%head(10)
## Warning: Expected 4 pieces. Additional pieces discarded in 10 rows [19, 26, 36, 51, 58,
## 59, 124, 173, 237, 238].
## Warning: Expected 4 pieces. Missing pieces filled with `NA` in 202 rows [3, 4, 5, 6, 7,
## 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23, 25, 27, ...].
Common_MisConcept_About_Infertility
## an1 an2 an3
## 1 Natural Spiritual Black magic
## 2 Supernatural Spiritual Black magic
## 3 Spiritual <NA> <NA>
## 4 Spiritual Black magic Curses by ancestors or deities
## 5 Natural Supernatural Spiritual
## 6 Natural Spiritual Black magic
## 7 Spiritual Black magic Curses by ancestors or deities
## 8 Natural Supernatural Black magic
## 9 Spiritual Black magic Curses from individuals
## 10 Spiritual Black magic <NA>
## an4
## 1 Curses by ancestors or deities
## 2 Curses by ancestors or deities
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## 7 <NA>
## 8 Curses by ancestors or deities
## 9 <NA>
## 10 <NA>
# Causes of Infertility Known by Respondent
Causes_Infertility_Known<-Perception_propt%>%
separate(X12..What.are.the.causes.of.infertility.that.you.know..Tick.as.many.as.apply.,c("an1","an2","an3","an4","an5","an6","an7","an8","an9","an10","an11"),sep = ";")%>%
select(an1,an2,an3,an4,an5,an6,an7,an8,an9,an10,an11)%>%head(10)
## Warning: Expected 11 pieces. Missing pieces filled with `NA` in 245 rows [3, 5, 6, 7, 8,
## 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, ...].
Causes_Infertility_Known
## an1 an2
## 1 Hormonal imbalance in Men Hormonal imbalance in women
## 2 Hormonal imbalance in women History of infection of genital tract in women
## 3 Hormonal imbalance in Men Hormonal imbalance in women
## 4 Hormonal imbalance in Men Hormonal imbalance in women
## 5 Hormonal imbalance in Men Hormonal imbalance in women
## 6 Hormonal imbalance in women History of infection of genital tract in men
## 7 Hormonal imbalance in Men Hormonal imbalance in women
## 8 Hormonal imbalance in Men Hormonal imbalance in women
## 9 Hormonal imbalance in men Hormonal imbalance in women
## 10 Hormonal imbalance in men Hormonal imbalance in women
## an3
## 1 History of infection of genital tract in men
## 2 Smoking
## 3 History of infection of genital tract in men
## 4 History of infection of genital tract in men
## 5 History of infection of genital tract in men
## 6 History of infection of genital tract in women
## 7 History of infection of genital tract in men
## 8 History of infection of genital tract in men
## 9 History of infection of genital tract in men
## 10 History of infection of genital tract in men
## an4
## 1 History of infection of genital tract in women
## 2 Environmental factor
## 3 History of infection of genital tract in women
## 4 History of infection of genital tract in women
## 5 History of infection of genital tract in women
## 6 Smoking
## 7 History of infection of genital tract in women
## 8 History of infection of genital tract in women
## 9 History of infection of genital tract in women
## 10 History of infection of genital tract in women
## an5 an6
## 1 Smoking Environmental factor
## 2 Use of family planning device by women Psychological stress
## 3 Smoking Environmental factor
## 4 Use of family planning device by women Psychological stress
## 5 Environmental factor Psychological stress
## 6 Use of family planning device by women Psychological stress
## 7 Use of family planning device by women Psychological stress
## 8 Use of family planning device by women Obesity in both men and wome
## 9 Blocked tube Drugs
## 10 Use of family planning device by women Natural (will of God)
## an7 an8
## 1 Psychological stress Obesity in both men and wome
## 2 Obesity in both men and wome Natural (will of God)
## 3 Obesity in both men and wome Blocked tube
## 4 Obesity in both men and wome Natural (will of God)
## 5 Obesity in both men and wome Natural (will of God)
## 6 Obesity in both men and wome Natural (will of God)
## 7 Obesity in both men and wome Natural (will of God)
## 8 Natural (will of God) Rhesus incompatibility
## 9 <NA> <NA>
## 10 Blocked tube Drugs
## an9 an10 an11
## 1 Natural (will of God) Blocked tube Drugs
## 2 Rhesus incompatibility Blocked tube Drugs
## 3 Drugs <NA> <NA>
## 4 Rhesus incompatibility Blocked tube Drugs
## 5 Blocked tube <NA> <NA>
## 6 Rhesus incompatibility Blocked tube <NA>
## 7 Blocked tube Drugs <NA>
## 8 Blocked tube Drugs <NA>
## 9 <NA> <NA> <NA>
## 10 <NA> <NA> <NA>
# Awareness of Hormonal Laboratory Investigation in Treatment of Infertility
Awareness_of_Hormonal_Laboratory_Investigation<-Perception_propt%>%
separate(X17..Are.you.aware.of.these.hormonal.laboratory.investigations.that.can.be.conducted.for.infertility.which.aids.in.the.treatment.in.both.men.and.women...Tick.as.many.as.apply.,c("an1","an2","an3","an4","an5","an6","an7"),sep = ";")%>%
select(an1,an2,an3,an4,an5,an6,an7)%>%head(10)
## Warning: Expected 7 pieces. Missing pieces filled with `NA` in 227 rows [1, 2, 3, 4, 6,
## 8, 9, 10, 11, 12, 13, 15, 16, 18, 19, 20, 21, 22, 23, 24, ...].
Awareness_of_Hormonal_Laboratory_Investigation
## an1 an2
## 1 Leutinizing Hormone (LH) Prolactin
## 2 Follicle Stimulating Hormone (FSH) Prolactin
## 3 Leutinizing Hormone (LH) Follicle Stimulating Hormone (FSH)
## 4 Follicle Stimulating Hormone (FSH) Estrogen
## 5 Leutinizing Hormone (LH) Follicle Stimulating Hormone (FSH)
## 6 Leutinizing Hormone (LH) Follicle Stimulating Hormone (FSH)
## 7 Leutinizing Hormone (LH) Follicle Stimulating Hormone (FSH)
## 8 Leutinizing Hormone (LH) Follicle Stimulating Hormone (FSH)
## 9 Leutinizing Hormone (LH) Follicle Stimulating Hormone (FSH)
## 10 Leutinizing Hormone (LH) Follicle Stimulating Hormone (FSH)
## an3 an4 an5 an6
## 1 Estrogen Progesterone Testosterone Anti-Mullerian hormone (AMH)
## 2 Estrogen Progesterone Testosterone <NA>
## 3 Prolactin Estrogen Progesterone Testosterone
## 4 Progesterone <NA> <NA> <NA>
## 5 Prolactin Estrogen Progesterone Testosterone
## 6 Prolactin Testosterone <NA> <NA>
## 7 Prolactin Estrogen Progesterone Testosterone
## 8 Estrogen Progesterone Testosterone <NA>
## 9 <NA> <NA> <NA> <NA>
## 10 Prolactin Estrogen Progesterone Testosterone
## an7
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 Anti-Mullerian hormone (AMH)
## 6 <NA>
## 7 Anti-Mullerian hormone (AMH)
## 8 <NA>
## 9 <NA>
## 10 <NA>
Feeling_After_Failing_Conception<-Perception_propt%>%
separate(X22..How.do.you.feel.when.you.are.not.able.to.conceive.after.1.year.of.unprotected.sexual.intercourse.with.your.partner..Tick.as.many.as.apply.,c("an1","an2","an3","an4","an5"),sep = ";")%>%
select(an1,an2,an3,an4,an5)%>%head(10)
## Warning: Expected 5 pieces. Missing pieces filled with `NA` in 247 rows [1, 2, 3, 4, 6,
## 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 20, 21, 22, 23, 24, ...].
Feeling_After_Failing_Conception
## an1 an2 an3 an4 an5
## 1 Sad Depressed Anxious Distress <NA>
## 2 Sad Depressed Anxious Distress <NA>
## 3 Anxious <NA> <NA> <NA> <NA>
## 4 Sad Depressed Anxious Distress <NA>
## 5 Sad Depressed Anxious Distress Suicidal thought
## 6 Sad Anxious <NA> <NA> <NA>
## 7 Sad Depressed Anxious Distress <NA>
## 8 Depressed <NA> <NA> <NA> <NA>
## 9 Sad Depressed Anxious Distress <NA>
## 10 Sad Anxious Distress <NA> <NA>
Table on the knowledge of the various treatment option available
(18)
#Treatment Options Known to respondence
Treatment_Options_Know<-Perception_propt%>%
mutate(Duration_infertility=recode(X6..Duration.of.infertility,"Nil:"="Nil"),
yes_no=ifelse(Duration_infertility=="Nil","Fertile","Infertile"))%>%
separate(X18..What.type.of.treatment.options.do.you.know..Tick.as.many.as.apply.,c("an1","an2","an3","an4","an5","an6","an7"),sep = ";")%>%
select(an1,an2,an3,an4,an5,an6,an7,yes_no)
## Warning: Expected 7 pieces. Additional pieces discarded in 19 rows [1, 3, 5, 8, 14, 20,
## 25, 40, 53, 131, 136, 152, 156, 161, 192, 198, 203, 240, 247].
## Warning: Expected 7 pieces. Missing pieces filled with `NA` in 223 rows [2, 6, 7, 9, 10,
## 11, 12, 13, 15, 16, 17, 18, 19, 22, 23, 24, 26, 27, 28, 29, ...].
# Treatment option of first respondence
treat1<-Treatment_Options_Know%>%group_by(yes_no)%>%
count(an1)%>%pivot_wider(names_from = yes_no,values_from = n)%>%
column_to_rownames(var="an1")%>%
mutate(Fertile=str_replace_na(Fertile,"0"),
Infertile=str_replace_na(Infertile,"0"),p_value=c("0.16","","","","",""))
treat1$Fertile<-as.integer(treat1$Fertile)
treat1$Infertile<-as.integer(treat1$Infertile)
treat2<-Treatment_Options_Know%>%
group_by(yes_no)%>%
count(an2)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
mutate(an2=str_replace_na(an2,"Not Selected"),Fertile=str_replace_na(Fertile,"0"),
Infertile=str_replace_na(Infertile,"0"),p_value=c("0.23","","","","","","",""))%>%
column_to_rownames(var="an2")
treat2$Fertile<-as.integer(treat2$Fertile)
treat2$Infertile<-as.integer(treat2$Infertile)
treat3<-Treatment_Options_Know%>%
group_by(yes_no)%>%
count(an3)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
mutate(an3=str_replace_na(an3,"Not Selected"),Fertile=str_replace_na(Fertile,"0"),
Infertile=str_replace_na(Infertile,"0"),p_value=c("0.03","","","","","","",""))%>%
column_to_rownames(var="an3")
treat3$Fertile<-as.integer(treat3$Fertile)
treat3$Infertile<-as.integer(treat3$Infertile)
treat4<-Treatment_Options_Know%>%
group_by(yes_no)%>%
count(an4)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
mutate(an4=str_replace_na(an4,"Not Selected"),Fertile=str_replace_na(Fertile,"0"),
Infertile=str_replace_na(Infertile,"0"),p_value=c("0.14","","","","","",""))%>%
column_to_rownames(var="an4")
treat4$Fertile<-as.integer(treat4$Fertile)
treat4$Infertile<-as.integer(treat4$Infertile)
treat5<-Treatment_Options_Know%>%
group_by(yes_no)%>%
count(an5)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
mutate(an5=str_replace_na(an5,"Not Selected"),Fertile=str_replace_na(Fertile,"0"),
Infertile=str_replace_na(Infertile,"0"),p_value=c("0.14","","","","","",""))%>%
column_to_rownames(var="an5")
treat5$Fertile<-as.integer(treat5$Fertile)
treat5$Infertile<-as.integer(treat5$Infertile)
treat6<-Treatment_Options_Know%>%
group_by(yes_no)%>%
count(an6)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
mutate(an6=str_replace_na(an6,"Not Selected"),Fertile=str_replace_na(Fertile,"0"),
Infertile=str_replace_na(Infertile,"0"),p_value=c("0.52","","","","","",""))%>%
column_to_rownames(var="an6")
treat6$Fertile<-as.integer(treat6$Fertile)
treat6$Infertile<-as.integer(treat6$Infertile)
treat7<-Treatment_Options_Know%>%
group_by(yes_no)%>%
count(an7)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
mutate(an7=str_replace_na(an7,"Not Selected"),Fertile=str_replace_na(Fertile,"0"),
Infertile=str_replace_na(Infertile,"0"),p_value=c("0.04","","","",""))%>%
column_to_rownames(var="an7")
treat7$Fertile<-as.integer(treat7$Fertile)
treat7$Infertile<-as.integer(treat7$Infertile)
bind_rows(ans_1=treat1,ans_2=treat2,ans_3=treat3,
ans_4=treat4,ans_5=treat5,ans_6=treat6,ans_7=treat7,.id = "Variables")
## Variables Fertile Infertile p_value
## In-vito fertilization (IVF)...1 ans_1 7 14 0.16
## Intra uterine insemination (IUI)...2 ans_1 2 0
## Intracytoplasmic sperm injection (ICS)...3 ans_1 1 0
## Sperm donor...4 ans_1 2 6
## Use of medication (Hormonal drugs) ans_1 61 164
## Surrogacy...6 ans_1 0 1
## In-vito fertilization (IVF)...7 ans_2 45 133 0.23
## Laparascopic/hysteroscopic surgery...8 ans_2 2 1
## Ova donor...9 ans_2 2 3
## Sperm donor...10 ans_2 8 15
## Surrogacy...11 ans_2 2 2
## Varicocelectomy...12 ans_2 1 0
## Not Selected...13 ans_2 13 30
## Intra uterine insemination (IUI)...14 ans_2 0 1
## Intracytoplasmic sperm injection (ICS)...15 ans_3 2 0 0.03
## Laparascopic/hysteroscopic surgery...16 ans_3 2 14
## Ova donor...17 ans_3 6 13
## Sperm donor...18 ans_3 34 56
## Surrogacy...19 ans_3 8 29
## Not Selected...20 ans_3 21 62
## Intra uterine insemination (IUI)...21 ans_3 0 1
## Varicocelectomy...22 ans_3 0 10
## Intra uterine insemination (IUI)...23 ans_4 4 6 0.14
## Laparascopic/hysteroscopic surgery...24 ans_4 2 17
## Ova donor...25 ans_4 22 41
## Ovarian stimulation...26 ans_4 1 0
## Surrogacy...27 ans_4 13 25
## Varicocelectomy...28 ans_4 1 9
## Not Selected...29 ans_4 30 87
## Intra uterine insemination (IUI)...30 ans_5 5 10 0.14
## Intracytoplasmic sperm injection (ICS)...31 ans_5 2 0
## Laparascopic/hysteroscopic surgery...32 ans_5 1 13
## Surrogacy...33 ans_5 20 34
## Not Selected...34 ans_5 45 121
## Ovarian stimulation...35 ans_5 0 1
## Varicocelectomy...36 ans_5 0 6
## Intra uterine insemination (IUI)...37 ans_6 13 20 0.52
## Laparascopic/hysteroscopic surgery...38 ans_6 3 5
## Ovarian stimulation...39 ans_6 2 3
## Not Selected...40 ans_6 55 149
## Intracytoplasmic sperm injection (ICS)...41 ans_6 0 1
## Tubal surgeries ans_6 0 1
## Varicocelectomy...43 ans_6 0 6
## Intra uterine insemination (IUI)...44 ans_7 2 3 0.04
## Intracytoplasmic sperm injection (ICS)...45 ans_7 7 2
## Laparascopic/hysteroscopic surgery...46 ans_7 2 6
## Varicocelectomy...47 ans_7 2 11
## Not Selected...48 ans_7 60 163
# Table 2 Knowledge and common misconceptions about factors that may affect sterility
# Common misconception about infertility
Common_MisConcept_About_Infertility<-Perception_propt%>%
mutate(Duration_infertility=recode(X6..Duration.of.infertility,"Nil:"="Nil"), yes_no=ifelse(Duration_infertility=="Nil","Fertile","Infertile"))%>%
separate(X13..Common.misconception.about.the.causes.of.infertility...Tick.as.many.as.apply.,c("an1","an2","an3","an4"),sep = ";")%>%
select(an1,an2,an3,an4,yes_no)
## Warning: Expected 4 pieces. Additional pieces discarded in 10 rows [19, 26, 36, 51, 58,
## 59, 124, 173, 237, 238].
## Warning: Expected 4 pieces. Missing pieces filled with `NA` in 202 rows [3, 4, 5, 6, 7,
## 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23, 25, 27, ...].
trt1<-Common_MisConcept_About_Infertility%>%group_by(yes_no)%>%
count(an1)%>%pivot_wider(names_from = yes_no,values_from = n)%>%
column_to_rownames(var="an1")%>%
mutate(Fertile=str_replace_na(Fertile,"0"),Infertile=str_replace_na(Infertile,"0"),p_value=c("0.10","","","","",""))
trt1$Fertile<-as.integer(trt1$Fertile)
trt1$Infertile<-as.integer(trt1$Infertile)
trt2<-Common_MisConcept_About_Infertility%>%
group_by(yes_no)%>%
count(an2)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
mutate(an2=str_replace_na(an2,"Not Selected"),Fertile=str_replace_na(Fertile,"0"),Infertile=str_replace_na(Infertile,"0"),p_value=c("0.06","","","","",""))%>%column_to_rownames(var="an2")
trt2$Fertile<-as.integer(trt2$Fertile)
trt2$Infertile<-as.integer(trt2$Infertile)
trt3<-Common_MisConcept_About_Infertility%>%
group_by(yes_no)%>%
count(an3)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
mutate(an3=str_replace_na(an3,"Not Selected"),Fertile=str_replace_na(Fertile,"0"),Infertile=str_replace_na(Infertile,"0"),p_value=c("0.45","","","","",""))%>%column_to_rownames(var="an3")
trt3$Fertile<-as.integer(trt3$Fertile)
trt3$Infertile<-as.integer(trt3$Infertile)
trt4<-Common_MisConcept_About_Infertility%>%
group_by(yes_no)%>%
count(an4)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
mutate(an4=str_replace_na(an4,"Not Selected"),Fertile=str_replace_na(Fertile,"0"),Infertile=str_replace_na(Infertile,"0"),p_value=c("0.31","","",""))%>%column_to_rownames(var="an4")
trt4$Fertile<-as.integer(trt4$Fertile)
trt4$Infertile<-as.integer(trt4$Infertile)
Misconception_Infertility<-bind_rows(ans_1=trt1,ans_2=trt2,ans_3=trt3,
ans_4=trt4,.id = "Variables")
Misconception_Infertility
## Variables Fertile Infertile p_value
## Black magic...1 ans_1 3 5 0.10
## Curses from individuals...2 ans_1 2 3
## Natural ans_1 38 88
## Others:...4 ans_1 1 0
## Spiritual...5 ans_1 28 89
## Supernatural...6 ans_1 1 0
## Black magic...7 ans_2 18 58 0.06
## Curses from individuals...8 ans_2 10 32
## Others:...9 ans_2 1 0
## Spiritual...10 ans_2 29 76
## Supernatural...11 ans_2 1 1
## Not Selected...12 ans_2 14 18
## Black magic...13 ans_3 26 46 0.45
## Curses from individuals...14 ans_3 13 61
## Others:...15 ans_3 2 0
## Not Selected...16 ans_3 32 75
## Curses by ancestors or deities...17 ans_3 0 2
## Spiritual...18 ans_3 0 1
## Curses by ancestors or deities...19 ans_4 3 0 0.31
## Curses from individuals...20 ans_4 19 30
## Others:...21 ans_4 1 3
## Not Selected...22 ans_4 50 152
circles1<-Misconception_Infertility%>%rownames_to_column("Treatment_Option")%>%select(-Variables,-p_value )%>%mutate(Treatment_Option=str_replace_all(Treatment_Option,"[...]\\d*",""),Treatment_Option=recode(Treatment_Option,"Obesity in both men and wome"="Obesity in both men and women"))%>%group_by(Treatment_Option)%>%
summarise(Fertile=sum(Fertile),Infertile=sum(Infertile))
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
library(ggpubr)
fig <- plot_ly(circles1, labels = ~Treatment_Option, values = ~Infertile, type = 'pie')
fig1 <- fig %>% layout(title = 'Treatment Options Known to Infertile Respondent', xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
fig1
figa <- plot_ly(circles1, labels = ~Treatment_Option, values = ~Fertile, type = 'pie')
fig <- figa %>% layout(title = 'Treatment Options Known to Fertile Respondent',xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
fig
### **Causes of Infertility Known by Respondent**
Causes_Infertility_Known<-Perception_propt%>%
mutate(Duration_infertility=recode(X6..Duration.of.infertility,"Nil:"="Nil"),
yes_no=ifelse(Duration_infertility=="Nil","Fertile","Infertile"))%>%
separate(X12..What.are.the.causes.of.infertility.that.you.know..Tick.as.many.as.apply.,c("an1","an2","an3","an4","an5","an6","an7","an8","an9","an10","an11"),sep = ";")%>%
select(an1,an2,an3,an4,an5,an6,an7,an8,an9,an10,an11,yes_no)
## Warning: Expected 11 pieces. Missing pieces filled with `NA` in 245 rows [3, 5, 6, 7, 8,
## 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, ...].
treta1<-Causes_Infertility_Known%>%group_by(yes_no)%>%
count(an1)%>%pivot_wider(names_from = yes_no,values_from = n)%>%
column_to_rownames(var="an1")%>%
mutate(Fertile=str_replace_na(Fertile,"0"),
Infertile=str_replace_na(Infertile,"0"),p_value=c("<0.001","","","","","","","","","",""))
treta1$Fertile<-as.integer(treta1$Fertile)
treta1$Infertile<-as.integer(treta1$Infertile)
treta2<-Causes_Infertility_Known%>%
group_by(yes_no)%>%
count(an2)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
mutate(an2=str_replace_na(an2,"Not Selected"),Fertile=str_replace_na(Fertile,"0"),
Infertile=str_replace_na(Infertile,"0"),
p_value=c("0.62","","","","","","","","",""))%>%column_to_rownames(var="an2")
treta2$Fertile<-as.integer(treta2$Fertile)
treta2$Infertile<-as.integer(treta2$Infertile)
treta3<-Causes_Infertility_Known%>%
group_by(yes_no)%>%
count(an3)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
mutate(an3=str_replace_na(an3,"Not Selected"),Fertile=str_replace_na(Fertile,"0"),
Infertile=str_replace_na(Infertile,"0"),p_value=c("<0.21","","","","","","","","","",""))%>%
column_to_rownames(var="an3")
treta3$Fertile<-as.integer(treta3$Fertile)
treta3$Infertile<-as.integer(treta3$Infertile)
# Error in fisher.test(.) :
# FEXACT error 7(location). LDSTP=18600 is too small for this problem,
# (pastp=39.6896, ipn_0:=ipoin[itp=336]=4340, stp[ipn_0]=39.0949).
# Increase workspace or consider using 'simulate.p.value=TRUE'
treta4<-Causes_Infertility_Known%>%
group_by(yes_no)%>%
count(an4)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
mutate(an4=str_replace_na(an4,"Not Selected"),Fertile=str_replace_na(Fertile,"0"),
Infertile=str_replace_na(Infertile,"0"),p_value=c("0.09","","","","","","","","",""))%>%
column_to_rownames(var="an4")
treta4$Fertile<-as.integer(treta4$Fertile)
treta4$Infertile<-as.integer(treta4$Infertile)
treta5<-Causes_Infertility_Known%>%
group_by(yes_no)%>%
count(an5)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
mutate(an5=str_replace_na(an5,"Not Selected"),Fertile=str_replace_na(Fertile,"0"),
Infertile=str_replace_na(Infertile,"0"),p_value=c("0.09","","","","","","","",""))%>%
column_to_rownames(var="an5")
treta5$Fertile<-as.integer(treta5$Fertile)
treta5$Infertile<-as.integer(treta5$Infertile)
treta6<-Causes_Infertility_Known%>%
group_by(yes_no)%>%
count(an6)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
mutate(an6=str_replace_na(an6,"Not Selected"),Fertile=str_replace_na(Fertile,"0"),
Infertile=str_replace_na(Infertile,"0"),p_value=c("0.26","","","","","","","",""))%>%
column_to_rownames(var="an6")
treta6$Fertile<-as.integer(treta6$Fertile)
treta6$Infertile<-as.integer(treta6$Infertile)
treta7<-Causes_Infertility_Known%>%
group_by(yes_no)%>%
count(an7)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
mutate(an7=str_replace_na(an7,"Not Selected"),Fertile=str_replace_na(Fertile,"0"),
Infertile=str_replace_na(Infertile,"0"),p_value=c("0.39","","","","","",""))%>%
column_to_rownames(var="an7")
treta7$Fertile<-as.integer(treta7$Fertile)
treta7$Infertile<-as.integer(treta7$Infertile)
treta8<-Causes_Infertility_Known%>%
group_by(yes_no)%>%
count(an8)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
mutate(an8=str_replace_na(an8,"Not Selected"),Fertile=str_replace_na(Fertile,"0"),
Infertile=str_replace_na(Infertile,"0"),p_value=c("0.15","","","","","",""))%>%
column_to_rownames(var="an8")
treta8$Fertile<-as.integer(treta8$Fertile)
treta8$Infertile<-as.integer(treta8$Infertile)
treta9<-Causes_Infertility_Known%>%
group_by(yes_no)%>%
count(an9)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
mutate(an9=str_replace_na(an9,"Not Selected"),Fertile=str_replace_na(Fertile,"0"),
Infertile=str_replace_na(Infertile,"0"),p_value=c("0.19","","","",""))%>%
column_to_rownames(var="an9")
treta9$Fertile<-as.integer(treta9$Fertile)
treta9$Infertile<-as.integer(treta9$Infertile)
treta10<-Causes_Infertility_Known%>%
group_by(yes_no)%>%
count(an10)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
mutate(an10=str_replace_na(an10,"Not Selected"),Fertile=str_replace_na(Fertile,"0"),
Infertile=str_replace_na(Infertile,"0"),p_value=c("0.02","",""))%>%
column_to_rownames(var="an10")
treta10$Fertile<-as.integer(treta10$Fertile)
treta10$Infertile<-as.integer(treta10$Infertile)
treta11<-Causes_Infertility_Known%>%
group_by(yes_no)%>%
count(an11)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
mutate(an11=str_replace_na(an11,"Not Selected"),Fertile=str_replace_na(Fertile,"0"),
Infertile=str_replace_na(Infertile,"0"),p_value=c("0.53",""))%>%
column_to_rownames(var="an11")
treta11$Fertile<-as.integer(treta11$Fertile)
treta11$Infertile<-as.integer(treta11$Infertile)
Known_cause<-bind_rows(ans_1=treta1,ans_2=treta2,ans_3=treta3,
ans_4=treta4,ans_5=treta5,ans_6=treta6,ans_7=treta7,
ans_8=treta8,ans_9=treta9,ans_10=treta10,
ans_11=treta11,.id = "Variables")
Known_cause
## Variables Fertile Infertile
## Environmental factor...1 ans_1 1 1
## History of infection of genital tract in men...2 ans_1 6 29
## History of infection of genital tract in women...3 ans_1 2 1
## Hormonal imbalance in Men ans_1 2 4
## Hormonal imbalance in men ans_1 46 113
## Hormonal imbalance in women...6 ans_1 9 29
## Natural (will of God)...7 ans_1 4 3
## Psychological stress...8 ans_1 1 1
## Use of family planning device by women...9 ans_1 2 1
## Blocked tube...10 ans_1 0 2
## Drugs...11 ans_1 0 1
## Drugs...12 ans_2 1 1
## Environmental factor...13 ans_2 1 2
## History of infection of genital tract in men...14 ans_2 6 27
## History of infection of genital tract in women...15 ans_2 11 26
## Hormonal imbalance in women...16 ans_2 46 115
## Natural (will of God)...17 ans_2 1 3
## Use of family planning device by women...18 ans_2 1 2
## Not Selected...19 ans_2 6 6
## Blocked tube...20 ans_2 0 2
## Obesity in both men and women...21 ans_2 0 1
## Drugs...22 ans_3 1 4
## Environmental factor...23 ans_3 3 12
## History of infection of genital tract in men...24 ans_3 40 91
## History of infection of genital tract in women...25 ans_3 8 25
## Natural (will of God)...26 ans_3 2 19
## Obesity in both men and women...27 ans_3 2 1
## Psychological stress...28 ans_3 1 1
## Smoking...29 ans_3 1 0
## Use of family planning device by women...30 ans_3 8 16
## Not Selected...31 ans_3 7 13
## Blocked tube...32 ans_3 0 3
## Blocked tube...33 ans_4 3 4
## Drugs...34 ans_4 1 16
## Environmental factor...35 ans_4 3 10
## History of infection of genital tract in women...36 ans_4 39 88
## Natural (will of God)...37 ans_4 5 13
## Obesity in both men and women...38 ans_4 1 2
## Psychological stress...39 ans_4 9 9
## Use of family planning device by women...40 ans_4 4 17
## Not Selected...41 ans_4 8 25
## Smoking...42 ans_4 0 1
## Blocked tube...43 ans_5 4 11
## Drugs...44 ans_5 4 12
## Environmental factor...45 ans_5 13 21
## Natural (will of God)...46 ans_5 7 31
## Obesity in both men and women...47 ans_5 9 5
## Psychological stress...48 ans_5 6 12
## Smoking...49 ans_5 1 1
## Use of family planning device by women...50 ans_5 16 41
## Not Selected...51 ans_5 13 51
## Blocked tube...52 ans_6 10 24
## Drugs...53 ans_6 4 15
## Environmental factor...54 ans_6 1 1
## Natural (will of God)...55 ans_6 15 24
## Obesity in both men and wome...56 ans_6 1 0
## Obesity in both men and women...57 ans_6 5 6
## Psychological stress...58 ans_6 7 18
## Use of family planning device by women...59 ans_6 10 17
## Not Selected...60 ans_6 20 80
## Blocked tube...61 ans_7 14 21
## Drugs...62 ans_7 8 24
## Natural (will of God)...63 ans_7 4 13
## Obesity in both men and wome...64 ans_7 1 5
## Obesity in both men and women...65 ans_7 5 8
## Psychological stress...66 ans_7 10 13
## Not Selected...67 ans_7 31 101
## Blocked tube...68 ans_8 4 13
## Drugs...69 ans_8 11 21
## Natural (will of God)...70 ans_8 7 12
## Obesity in both men and wome...71 ans_8 1 0
## Obesity in both men and women...72 ans_8 7 8
## Rhesus incompatibility...73 ans_8 1 0
## Not Selected...74 ans_8 42 131
## Blocked tube...75 ans_9 9 9
## Drugs...76 ans_9 4 14
## Natural (will of God)...77 ans_9 5 7
## Rhesus incompatibility...78 ans_9 1 2
## Not Selected...79 ans_9 54 153
## Blocked tube...80 ans_10 6 9
## Drugs...81 ans_10 9 7
## Not Selected...82 ans_10 58 169
## Drugs...83 ans_11 5 8
## Not Selected...84 ans_11 68 177
## p_value
## Environmental factor...1 <0.001
## History of infection of genital tract in men...2
## History of infection of genital tract in women...3
## Hormonal imbalance in Men
## Hormonal imbalance in men
## Hormonal imbalance in women...6
## Natural (will of God)...7
## Psychological stress...8
## Use of family planning device by women...9
## Blocked tube...10
## Drugs...11
## Drugs...12 0.62
## Environmental factor...13
## History of infection of genital tract in men...14
## History of infection of genital tract in women...15
## Hormonal imbalance in women...16
## Natural (will of God)...17
## Use of family planning device by women...18
## Not Selected...19
## Blocked tube...20
## Obesity in both men and women...21
## Drugs...22 <0.21
## Environmental factor...23
## History of infection of genital tract in men...24
## History of infection of genital tract in women...25
## Natural (will of God)...26
## Obesity in both men and women...27
## Psychological stress...28
## Smoking...29
## Use of family planning device by women...30
## Not Selected...31
## Blocked tube...32
## Blocked tube...33 0.09
## Drugs...34
## Environmental factor...35
## History of infection of genital tract in women...36
## Natural (will of God)...37
## Obesity in both men and women...38
## Psychological stress...39
## Use of family planning device by women...40
## Not Selected...41
## Smoking...42
## Blocked tube...43 0.09
## Drugs...44
## Environmental factor...45
## Natural (will of God)...46
## Obesity in both men and women...47
## Psychological stress...48
## Smoking...49
## Use of family planning device by women...50
## Not Selected...51
## Blocked tube...52 0.26
## Drugs...53
## Environmental factor...54
## Natural (will of God)...55
## Obesity in both men and wome...56
## Obesity in both men and women...57
## Psychological stress...58
## Use of family planning device by women...59
## Not Selected...60
## Blocked tube...61 0.39
## Drugs...62
## Natural (will of God)...63
## Obesity in both men and wome...64
## Obesity in both men and women...65
## Psychological stress...66
## Not Selected...67
## Blocked tube...68 0.15
## Drugs...69
## Natural (will of God)...70
## Obesity in both men and wome...71
## Obesity in both men and women...72
## Rhesus incompatibility...73
## Not Selected...74
## Blocked tube...75 0.19
## Drugs...76
## Natural (will of God)...77
## Rhesus incompatibility...78
## Not Selected...79
## Blocked tube...80 0.02
## Drugs...81
## Not Selected...82
## Drugs...83 0.53
## Not Selected...84
circles<-Known_cause%>%rownames_to_column("Treatment_Option")%>%
select(-Variables,-p_value )%>%mutate(Treatment_Option=str_replace_all(Treatment_Option,"[...]\\d*",""),Treatment_Option=recode(Treatment_Option,"Obesity in both men and wome"="Obesity in both men and women"))%>%group_by(Treatment_Option)%>%
summarise(Fertile=sum(Fertile),Infertile=sum(Infertile))
library(plotly)
library(ggpubr)
fig2a <- plot_ly(circles, labels = ~Treatment_Option, values = ~Infertile, type = 'pie')
fig2 <- fig2a %>% layout(title = 'Treatment Options Known to Infertile Respondent',
xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
fig2
fig3a <- plot_ly(circles, labels = ~Treatment_Option, values = ~Fertile, type = 'pie')
fig3 <- fig3a %>% layout(title = 'Treatment Options Known to Fertile Respondent',
xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
fig3
ggplot(circles,aes(x="",y=Infertile,fill=Treatment_Option))+
geom_bar(width = 1,stat = "identity")+coord_polar("y",start = 0)+
theme_void()+scale_fill_grey()+
theme(axis.title.x = element_blank())

# Awareness of Hormonal Laboratory Investigation in Treatment of Infertility
Awareness_of_Hormonal_Laboratory_Investigation<-Perception_propt%>%
mutate(Duration_infertility=recode(X6..Duration.of.infertility,"Nil:"="Nil"),
yes_no=ifelse(Duration_infertility=="Nil","Fertile","Infertile"))%>%
separate(X17..Are.you.aware.of.these.hormonal.laboratory.investigations.that.can.be.conducted.for.infertility.which.aids.in.the.treatment.in.both.men.and.women...Tick.as.many.as.apply.,c("an1","an2","an3","an4","an5","an6","an7"),sep = ";")%>%
select(an1,an2,an3,an4,an5,an6,an7,yes_no)
## Warning: Expected 7 pieces. Missing pieces filled with `NA` in 227 rows [1, 2, 3, 4, 6,
## 8, 9, 10, 11, 12, 13, 15, 16, 18, 19, 20, 21, 22, 23, 24, ...].
tret1<-Awareness_of_Hormonal_Laboratory_Investigation%>%group_by(yes_no)%>%
count(an1)%>%pivot_wider(names_from = yes_no,values_from = n)%>%
column_to_rownames(var="an1")%>%
mutate(Fertile=str_replace_na(Fertile,"0"),Infertile=str_replace_na(Infertile,"0"),p_value=c("0.16","","","","",""))
tret1$Fertile<-as.integer(tret1$Fertile)
tret1$Infertile<-as.integer(tret1$Infertile)
tret2<-Awareness_of_Hormonal_Laboratory_Investigation%>%
group_by(yes_no)%>%
count(an2)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
mutate(an2=str_replace_na(an2,"Not Selected"),Fertile=str_replace_na(Fertile,"0"),
Infertile=str_replace_na(Infertile,"0"),p_value=c("0.01","","","","",""))%>%column_to_rownames(var="an2")
tret2$Fertile<-as.integer(tret2$Fertile)
tret2$Infertile<-as.integer(tret2$Infertile)
tret3<-Awareness_of_Hormonal_Laboratory_Investigation%>%
group_by(yes_no)%>%
count(an3)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
mutate(an3=str_replace_na(an3,"Not Selected"),Fertile=str_replace_na(Fertile,"0"),
Infertile=str_replace_na(Infertile,"0"),p_value=c("<0.001","","","","",""))%>%column_to_rownames(var="an3")
tret3$Fertile<-as.integer(tret3$Fertile)
tret3$Infertile<-as.integer(tret3$Infertile)
tret4<-Awareness_of_Hormonal_Laboratory_Investigation%>%
group_by(yes_no)%>%
count(an4)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
mutate(an4=str_replace_na(an4,"Not Selected"),Fertile=str_replace_na(Fertile,"0"),Infertile=str_replace_na(Infertile,"0"),p_value=c("0.18","","","",""))%>%
column_to_rownames(var="an4")
tret4$Fertile<-as.integer(tret4$Fertile)
tret4$Infertile<-as.integer(tret4$Infertile)
tret5<-Awareness_of_Hormonal_Laboratory_Investigation%>%
group_by(yes_no)%>%
count(an5)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
mutate(an5=str_replace_na(an5,"Not Selected"),Fertile=str_replace_na(Fertile,"0"),
Infertile=str_replace_na(Infertile,"0"),p_value=c("0.02","","",""))%>%column_to_rownames(var="an5")
tret5$Fertile<-as.integer(tret5$Fertile)
tret5$Infertile<-as.integer(tret5$Infertile)
tret6<-Awareness_of_Hormonal_Laboratory_Investigation%>%
group_by(yes_no)%>%
count(an6)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
mutate(an6=str_replace_na(an6,"Not Selected"),Fertile=str_replace_na(Fertile,"0"),
Infertile=str_replace_na(Infertile,"0"),p_value=c("0.18","",""))%>%
column_to_rownames(var="an6")
tret6$Fertile<-as.integer(tret6$Fertile)
tret6$Infertile<-as.integer(tret6$Infertile)
tret7<-Awareness_of_Hormonal_Laboratory_Investigation%>%
group_by(yes_no)%>%
count(an7)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
mutate(an7=str_replace_na(an7,"Not Selected"),Fertile=str_replace_na(Fertile,"0"),
Infertile=str_replace_na(Infertile,"0"),p_value=c("0.94",""))%>%
column_to_rownames(var="an7")
tret7$Fertile<-as.integer(tret7$Fertile)
tret7$Infertile<-as.integer(tret7$Infertile)
Treatment_Option<-bind_rows(ans_1=tret1,ans_2=tret2,ans_3=tret3,
ans_4=tret4,ans_5=tret5,ans_6=tret6,ans_7=tret7,.id = "Variables")
circles2<-Treatment_Option%>%rownames_to_column("Treatment_Option")%>%
select(-Variables,-p_value )%>%mutate(Treatment_Option=str_replace_all(Treatment_Option,"[...]\\d*",""),Treatment_Option=recode(Treatment_Option,"Obesity in both men and wome"="Obesity in both men and women"))%>%group_by(Treatment_Option)%>%
summarise(Fertile=sum(Fertile),Infertile=sum(Infertile))
fig4a <- plot_ly(circles2, labels = ~Treatment_Option, values = ~Infertile, type = 'pie')
fig4 <- fig4a %>% layout(title = 'Treatment Options Known to Infertile Respondent',xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
fig4
fig5a <- plot_ly(circles2, labels = ~Treatment_Option, values = ~Fertile, type = 'pie')
fig5 <- fig5a %>% layout(title = 'Treatment Options Known to Fertile Respondent',xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
fig5
# Feeling After Failing Conception
Feeling_After_Failing_Conception<-Perception_propt%>%
mutate(Duration_infertility=recode(X6..Duration.of.infertility,"Nil:"="Nil"),
yes_no=ifelse(Duration_infertility=="Nil","Fertile","Infertile"))%>%
separate(X22..How.do.you.feel.when.you.are.not.able.to.conceive.after.1.year.of.unprotected.sexual.intercourse.with.your.partner..Tick.as.many.as.apply.,c("an1","an2","an3","an4","an5"),sep = ";")%>%
select(an1,an2,an3,an4,an5,yes_no)
## Warning: Expected 5 pieces. Missing pieces filled with `NA` in 247 rows [1, 2, 3, 4, 6,
## 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 20, 21, 22, 23, 24, ...].
trat1<-Feeling_After_Failing_Conception%>%group_by(yes_no)%>%
count(an1)%>%pivot_wider(names_from = yes_no,values_from = n)%>%
column_to_rownames(var="an1")%>%
mutate(Fertile=str_replace_na(Fertile,"0"),
Infertile=str_replace_na(Infertile,"0"),p_value=c("<0.001","","",""))
trat1$Fertile<-as.integer(trat1$Fertile)
trat1$Infertile<-as.integer(trat1$Infertile)
trat2<-Feeling_After_Failing_Conception%>%
group_by(yes_no)%>%
count(an2)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
mutate(an2=str_replace_na(an2,"Not Selected"),Fertile=str_replace_na(Fertile,"0"),
Infertile=str_replace_na(Infertile,"0"),p_value=c("0.002","","",""))%>%column_to_rownames(var="an2")
trat2$Fertile<-as.integer(trat2$Fertile)
trat2$Infertile<-as.integer(trat2$Infertile)
trat3<-Feeling_After_Failing_Conception%>%
group_by(yes_no)%>%
count(an3)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
mutate(an3=str_replace_na(an3,"Not Selected"),Fertile=str_replace_na(Fertile,"0"),
Infertile=str_replace_na(Infertile,"0"),p_value=c("0.45","","",""))%>%
column_to_rownames(var="an3")
trat3$Fertile<-as.integer(trat3$Fertile)
trat3$Infertile<-as.integer(trat3$Infertile)
trat4<-Feeling_After_Failing_Conception%>%
group_by(yes_no)%>%
count(an4)%>%
pivot_wider(names_from = yes_no,values_from = n)%>%
mutate(an4=str_replace_na(an4,"Not Selected"),Fertile=str_replace_na(Fertile,"0"),
Infertile=str_replace_na(Infertile,"0"),p_value=c("0.25",""))%>%
column_to_rownames(var="an4")
trat4$Fertile<-as.integer(trat4$Fertile)
trat4$Infertile<-as.integer(trat4$Infertile)
Feeling_Failing<-bind_rows(ans_1=trat1,ans_2=trat2,ans_3=trat3,
ans_4=trat4,.id = "Variables")
circles3<-Feeling_Failing%>%rownames_to_column("Treatment_Option")%>%
select(-Variables,-p_value )%>%mutate(Treatment_Option=str_replace_all(Treatment_Option,"[...]\\d*",""),Treatment_Option=recode(Treatment_Option,"Obesity in both men and wome"="Obesity in both men and women"))%>%group_by(Treatment_Option)%>%
summarise(Fertile=sum(Fertile),Infertile=sum(Infertile))
fig6a <- plot_ly(circles3, labels = ~Treatment_Option, values = ~Infertile, type = 'pie')
fig6 <- fig6a %>% layout(title = 'Treatment Options Known to Infertile Respondent',
xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
fig6
fig7a <- plot_ly(circles2, labels = ~Treatment_Option, values = ~Fertile, type = 'pie')
fig7 <- fig7a %>% layout(title = 'Treatment Options Known to Fertile Respondent',
xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
fig7